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library(ggplot2)
library(plotly)

package 㤼㸱plotly㤼㸲 was built under R version 4.0.4Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
Registered S3 methods overwritten by 'htmltools':
  method               from         
  print.html           tools:rstudio
  print.shiny.tag      tools:rstudio
  print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

Attaching package: 㤼㸱plotly㤼㸲

The following object is masked from 㤼㸱package:ggplot2㤼㸲:

    last_plot

The following object is masked from 㤼㸱package:stats㤼㸲:

    filter

The following object is masked from 㤼㸱package:graphics㤼㸲:

    layout

source("./DataProccess.R")

Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
-- Attaching packages -------------------------------------------------------------------------------------------------------------- tidyverse 1.3.0 --
v tibble  3.0.5     v dplyr   1.0.3
v tidyr   1.1.2     v stringr 1.4.0
v readr   1.4.0     v forcats 0.5.0
v purrr   0.3.4     
-- Conflicts ----------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
x dplyr::filter() masks plotly::filter(), stats::filter()
x dplyr::lag()    masks stats::lag()

Attaching package: 㤼㸱zoo㤼㸲

The following objects are masked from 㤼㸱package:base㤼㸲:

    as.Date, as.Date.numeric

Loading required package: ggstance

Attaching package: 㤼㸱ggstance㤼㸲

The following objects are masked from 㤼㸱package:ggplot2㤼㸲:

    geom_errorbarh, GeomErrorbarh

Loading required package: scales

Attaching package: 㤼㸱scales㤼㸲

The following object is masked from 㤼㸱package:purrr㤼㸲:

    discard

The following object is masked from 㤼㸱package:readr㤼㸲:

    col_factor

Loading required package: ggridges

New to ggformula?  Try the tutorials: 
    learnr::run_tutorial("introduction", package = "ggformula")
    learnr::run_tutorial("refining", package = "ggformula")

Attaching package: 㤼㸱lubridate㤼㸲

The following objects are masked from 㤼㸱package:base㤼㸲:

    date, intersect, setdiff, union

HFGData="../data/HFG data for stats preliminary 3-18-21.xlsx"

HFGFrame=HFGInfo(HFGData)%>%
  mutate("Filter Rep"=as.character(`Filter Rep`),`Well Rep`=as.character(`Well Rep`))

New names:
* `` -> ...2

HFGFrame.mean=HFGFrame%>%
  ungroup()%>%
  group_by(Date,`Filter Rep`,Plant)%>%
  summarise(N1GC=mean(N1GC))%>%
  ungroup()%>%
  filter(N1GC<10000000)

`summarise()` has grouped output by 'Date', 'Filter Rep'. You can override using the `.groups` argument.

HFGFrame.Filtered=HFGFrame%>%
  filter(N1GC<100000000)%>%
  filter(!is.na(N1GC),!is.na(Date))


HFGDFrame=HFGFrame.Filtered

p=ggplot()+
geom_jitter(data=HFGDFrame,aes(x=Date, y=N1GC , color=`Filter Rep`,text=Plant),size=1, position = position_jitter(width = .1, height = 0))+
  geom_smooth(data=HFGFrame.mean,aes(x=Date,y=N1GC, color=`Filter Rep`),se=F)

Ignoring unknown aesthetics: text

ggplotly(p,tooltip = c("text", "size"))

`geom_smooth()` using method = 'loess' and formula 'y ~ x'